Abstract
As the continuous development of mobile social networks, the structure of the mobile social network increasingly becomes complex. It not only speeds up information transmission between people but also expands the scope of information exchange, which has become an essential and important social media in people’s social life. How to effectively identify and classify these online communities has important practical significance for the study of social networks. Correctly detecting the community structure of mobile social networks can not only improve the accuracy of friend recommendation, link prediction, service user positioning, product marketing, and other aspects but also provide an important basis for the monitoring of online public opinion. But the traditional social network cluster method based on the trust degree mainly calculates the user trust by analyzing the interactive feedback information between users. This method cannot effectively solve the “cold start” problem in the trust calculation process, that is, for the new network node, the trust value of this node cannot be accurately measured due to the lack of interaction with other nodes. Focusing on this problem, we propose a Gaussian pigeon-oriented graph clustering algorithm for social networks’ cluster in this paper. A graph model is first built. Then, an efficient K-medoid algorithm is utilized to search the user center in all groups. The Gaussian pigeon algorithm is used to search the similarity between each user and the central user. Users that meet the similarity threshold are divided into the same user group. Finally, the simulation results show that the proposed method has better cluster effect than other state-of-the-art social networks’ clustering approaches.
Highlights
In social networks, users are information acquirers, and information publishers and transmitters. The emergence of this information transmission mechanism greatly reduces the social cost among network users, but enables users to establish social relations with some people with common characteristics through online activities, which forms a network structure similar to that of real social communities in mobile social networks [1], [2]
Sun et al.: GPOGC: Gaussian Pigeon-Oriented Graph Clustering Algorithm for Social Networks Cluster a directed network clustering algorithm based on structural similarity
Gaussian Pigeon optimized algorithm and proposed social networks cluster method are detailed illustrated in section 4 and section 5, respectively
Summary
Users are information acquirers, and information publishers and transmitters. In addition to the social network clustering algorithm based on the density of links, there are clustering algorithms that take node diversity, strong and weak social relations and various hidden information into consideration. Y. Sun et al.: GPOGC: Gaussian Pigeon-Oriented Graph Clustering Algorithm for Social Networks Cluster a directed network clustering algorithm based on structural similarity. References [8], [9] all took social network structure as the basis of clustering, but only direct social relationship was considered in the process of network construction. In order to solve the above problems, GPOGC: Gaussian Pigeonoriented graph clustering algorithm for social networks clustering is proposed. Gaussian Pigeon optimized algorithm and proposed social networks cluster method are detailed illustrated in section 4 and section 5, respectively.
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